Blind Spectrum Sensing Scheme Based on Harmonic Mean of Eigenvalues of Sample Covariance Matrix

Maximum eigenvalue-harmonic mean (MEHM) detection is a very promising algorithm for spectrum sensing in cognitive radio. However, the asymptotic analysis method currently used greatly limits the improvement of the detection performance of MEHM. For this, a novel maximum eigenvalue-harmonic mean (NMEHM) based spectrum sensing algorithm is proposed, using the results of the distribution of the limiting eigenvalues of the sample covariance matrix in random matrix theory. The proposed algorithm significantly improves the detection performance of the traditional MEHM algorithm and achieves better detection results than the classical eigenvalues based algorithms; meanwhile, the proposed algorithm does not require a priori knowledge of the primary user signal and wireless channel, which can effectively overcome the impact of noise uncertainty. Simulation results demonstrate the validity of the proposed algorithm.